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https://issues.apache.org/jira/browse/MAHOUT-237?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12799557#action_12799557
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Jake Mannix commented on MAHOUT-237:
------------------------------------

Given the following code in PartialVectorGenerator:

{code}        
for (Entry<String,MutableInt> pair : termFrequency.entrySet()) {
  String tk = pair.getKey();
  if (dictionary.containsKey(tk) == false) continue;
  vector.setQuick(dictionary.get(tk).intValue(), pair.getValue().doubleValue());
}             
assert (vector.getNumNondefaultElements() == termFrequency.size());
{code}

Why is this assert expected to pass?  If there are dictionary.containsKey(tk) 
ever returns true, this will fail...

> Map/Reduce Implementation of Document Vectorizer
> ------------------------------------------------
>
>                 Key: MAHOUT-237
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-237
>             Project: Mahout
>          Issue Type: New Feature
>    Affects Versions: 0.3
>            Reporter: Robin Anil
>            Assignee: Robin Anil
>             Fix For: 0.3
>
>         Attachments: DictionaryVectorizer.patch, DictionaryVectorizer.patch, 
> DictionaryVectorizer.patch, DictionaryVectorizer.patch, 
> DictionaryVectorizer.patch, SparseVector-VIntWritable.patch
>
>
> Current Vectorizer uses Lucene Index to convert documents into SparseVectors
> Ted is working on a Hash based Vectorizer which can map features into Vectors 
> of fixed size and sum it up to get the document Vector
> This is a pure bag-of-words based Vectorizer written in Map/Reduce. 
> The input document is in SequenceFile<Text,Text> . with key = docid, value = 
> content
> First Map/Reduce over the document collection and generate the feature counts.
> Second Sequential pass reads the output of the map/reduce and converts them 
> to SequenceFile<Text, LongWritable> where key=feature, value = unique id 
>     Second stage should create shards of features of a given split size
> Third Map/Reduce over the document collection, using each shard and create 
> Partial(containing the features of the given shard) SparseVectors 
> Fourth Map/Reduce over partial shard, group by docid, create full document 
> Vector

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